Model-Based Reconstruction for Magnetic Particle Imaging in 2D and 3D

März, Thomas; Weinmann, Andreas

doi:10.48550/arxiv.1605.08095

Cited by 1 publication

(2 citation statements)

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“…Reconstructing the concentration vector c from given measurement vector û is a classic inverse problem. In [17], [25], it was shown that the multidimensional MPI reconstruction problem is severely ill-posed. Thus, computing a stable and unique solution requires regularization.…”

Section: Mpi Forward Modelmentioning

confidence: 99%

“…The most frequently used magnetization model is the Langevin-or equilibrium model, which is also the basis for the derivations in the following articles. In [25], the authors derive analytical reconstruction formulae as well as numerical reconstruction schemes for two-and three-dimensional MPI. They examine and compare the illposedness of the reconstruction problem for different dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Magnetic Particle Imaging for Dynamic Tracer Distributions

Brandt¹,

Schmidt²

2021

Preprint

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Magnetic Particle Imaging (MPI) is a promising tracer-based, functional medical imaging technique which measures the non-linear response of superparamagnetic iron oxide nanoparticles (SPION) to a dynamic magnetic field. For image reconstruction, system matrices from time-consuming calibration scans are used predominantly. Finding modeled forward operators for magnetic particle imaging, which are able to compete with measured matrices in practice, is an ongoing topic of research. The existing models for magnetic particle imaging are by design not suitable for dynamic tracer concentrations. Neither modeled nor measured system matrices account for changes in the concentration during a single scanning cycle. In this paper we present a new MPI forward model for dynamic concentrations. A standard model will be introduced briefly, followed by the changes due to the dynamic behavior of the tracer concentration. Furthermore, the relevance of this new extended model is examined by investigating the influence of the extension and example reconstructions with the new and the standard model.

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Section: Mpi Forward Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%